International Conference on Computer and Knowledge Engineering

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Parallel Local Feature Selection For High-dimensional Data

Authors :

Zhaleh Manbari¹ Chiman Salavati² Fardin AkhlaghianTab³ Barzan Saeedpoor⁴ Himan Delbina⁵ Mahmud Abdulla Mohammad⁶

1- Department of Computer Engineering University of Kurdistan Sanandaj, Iran 2- Department of Computer Engineering University of Kurdistan Sanandaj, Iran 3- Department of Computer Engineering University of Kurdistan Sanandaj, Iran 4- Department of Computer Engineering University of Kurdistan Sanandaj, Iran 5- RD Department West E-swap Co. 6- College of Basic Education, Computer Science Department, University of Raparin, Ranya, Kurdistan Region, Iraq

Keywords :

Local feature selection, High dimensional data, Concept drift, Parallel processing

Abstract :

Recent technological progress has expanded high-dimensional datasets. This phenomenon along with irrelevant and redundant features is led to a challenging feature selection process. The main purpose of feature selection is to reduce the dimensions of such datasets by eliminating non-essential and irrelevant features which can improve the performance of learning algorithms. An existing major challenge is that most of the feature selection methods intend to select a global feature subset that is applied over all the sample space. As each region of the sample space, with a special set of features, responds to the patterns correctly, the global feature selection methods are not efficient. In this paper, a novel scheme of localized feature selection is presented in which by parallel processing and distribution of data, classification is performed. In the proposed method, each region of the sample space is associated with its own distinct optimized feature set. The simulation results on several real-world datasets on SVM, NB, and DT classifiers demonstrate that the proposed local feature selection is superior to global feature selection methods.